Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory161.1 B

Variable types

Text1
Numeric12

Alerts

Agility is highly overall correlated with Durability and 5 other fieldsHigh correlation
Analytical Aptitude is highly overall correlated with Hand-eye coordination and 2 other fieldsHigh correlation
Durability is highly overall correlated with Agility and 5 other fieldsHigh correlation
Endurance is highly overall correlated with Durability and 3 other fieldsHigh correlation
Flexibility is highly overall correlated with Agility and 2 other fieldsHigh correlation
Hand-eye coordination is highly overall correlated with Agility and 1 other fieldsHigh correlation
Nerve is highly overall correlated with Durability and 2 other fieldsHigh correlation
Power is highly overall correlated with Rank and 3 other fieldsHigh correlation
Rank is highly overall correlated with Agility and 9 other fieldsHigh correlation
Speed is highly overall correlated with Agility and 4 other fieldsHigh correlation
Strength is highly overall correlated with Durability and 3 other fieldsHigh correlation
Total is highly overall correlated with Agility and 9 other fieldsHigh correlation
Rank is uniformly distributedUniform
SPORT has unique valuesUnique

Reproduction

Analysis started2025-11-24 15:05:21.495335
Analysis finished2025-11-24 15:05:33.358934
Duration11.86 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

SPORT
Text

Unique 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-11-24T16:05:33.510112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length26
Mean length13.933333
Min length4

Characters and Unicode

Total characters836
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st rowBoxing
2nd rowIce Hockey
3rd rowFootball
4th rowBasketball
5th rowWrestling
ValueCountFrequency (%)
field8
 
6.9%
and7
 
6.0%
track7
 
6.0%
distance4
 
3.4%
skiing4
 
3.4%
sprints3
 
2.6%
skating3
 
2.6%
rodeo3
 
2.6%
strokes2
 
1.7%
all2
 
1.7%
Other values (66)73
62.9%
2025-11-24T16:05:33.792978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i73
 
8.7%
e61
 
7.3%
n59
 
7.1%
a58
 
6.9%
56
 
6.7%
l53
 
6.3%
g38
 
4.5%
r37
 
4.4%
o36
 
4.3%
t34
 
4.1%
Other values (42)331
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i73
 
8.7%
e61
 
7.3%
n59
 
7.1%
a58
 
6.9%
56
 
6.7%
l53
 
6.3%
g38
 
4.5%
r37
 
4.4%
o36
 
4.3%
t34
 
4.1%
Other values (42)331
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i73
 
8.7%
e61
 
7.3%
n59
 
7.1%
a58
 
6.9%
56
 
6.7%
l53
 
6.3%
g38
 
4.5%
r37
 
4.4%
o36
 
4.3%
t34
 
4.1%
Other values (42)331
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i73
 
8.7%
e61
 
7.3%
n59
 
7.1%
a58
 
6.9%
56
 
6.7%
l53
 
6.3%
g38
 
4.5%
r37
 
4.4%
o36
 
4.3%
t34
 
4.1%
Other values (42)331
39.6%

Endurance
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0776667
Minimum1
Maximum9.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:33.886096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q13.5
median4.63
Q36.66
95-th percentile9.0125
Maximum9.63
Range8.63
Interquartile range (IQR)3.16

Descriptive statistics

Standard deviation2.0942167
Coefficient of variation (CV)0.41243682
Kurtosis-0.49086104
Mean5.0776667
Median Absolute Deviation (MAD)1.38
Skewness0.44452444
Sum304.66
Variance4.3857436
MonotonicityNot monotonic
2025-11-24T16:05:33.983808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4.134
 
6.7%
3.54
 
6.7%
4.634
 
6.7%
6.753
 
5.0%
3.253
 
5.0%
2.253
 
5.0%
43
 
5.0%
7.252
 
3.3%
2.882
 
3.3%
9.632
 
3.3%
Other values (26)30
50.0%
ValueCountFrequency (%)
11
 
1.7%
1.381
 
1.7%
2.253
5.0%
2.882
3.3%
31
 
1.7%
3.131
 
1.7%
3.253
5.0%
3.382
3.3%
3.54
6.7%
3.631
 
1.7%
ValueCountFrequency (%)
9.632
3.3%
9.251
1.7%
91
1.7%
8.631
1.7%
8.131
1.7%
7.881
1.7%
7.751
1.7%
7.631
1.7%
7.381
1.7%
7.252
3.3%

Strength
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.169
Minimum1
Maximum9.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:34.072285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.88
median5.19
Q36.13
95-th percentile8.1425
Maximum9.25
Range8.25
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.7153571
Coefficient of variation (CV)0.33185472
Kurtosis0.061822547
Mean5.169
Median Absolute Deviation (MAD)1.125
Skewness0.066523029
Sum310.14
Variance2.9424498
MonotonicityNot monotonic
2025-11-24T16:05:34.160988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5.256
 
10.0%
5.136
 
10.0%
4.54
 
6.7%
3.883
 
5.0%
3.753
 
5.0%
2.52
 
3.3%
5.382
 
3.3%
3.252
 
3.3%
52
 
3.3%
72
 
3.3%
Other values (26)28
46.7%
ValueCountFrequency (%)
11
 
1.7%
1.631
 
1.7%
2.52
3.3%
2.631
 
1.7%
2.751
 
1.7%
3.252
3.3%
3.381
 
1.7%
3.51
 
1.7%
3.631
 
1.7%
3.753
5.0%
ValueCountFrequency (%)
9.251
1.7%
8.631
1.7%
8.381
1.7%
8.131
1.7%
7.881
1.7%
7.751
1.7%
7.251
1.7%
7.131
1.7%
72
3.3%
6.881
1.7%

Power
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5091667
Minimum1.25
Maximum9.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:34.253445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.25
5-th percentile1.75
Q14.0975
median5.69
Q37.13
95-th percentile8.155
Maximum9.75
Range8.5
Interquartile range (IQR)3.0325

Descriptive statistics

Standard deviation1.9803974
Coefficient of variation (CV)0.35947313
Kurtosis-0.48874043
Mean5.5091667
Median Absolute Deviation (MAD)1.44
Skewness-0.23920537
Sum330.55
Variance3.9219739
MonotonicityNot monotonic
2025-11-24T16:05:34.353290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7.134
 
6.7%
6.633
 
5.0%
7.883
 
5.0%
4.633
 
5.0%
43
 
5.0%
6.52
 
3.3%
7.252
 
3.3%
1.752
 
3.3%
3.382
 
3.3%
3.752
 
3.3%
Other values (29)34
56.7%
ValueCountFrequency (%)
1.251
 
1.7%
1.381
 
1.7%
1.752
3.3%
2.51
 
1.7%
2.631
 
1.7%
2.881
 
1.7%
3.131
 
1.7%
3.382
3.3%
3.752
3.3%
43
5.0%
ValueCountFrequency (%)
9.751
 
1.7%
9.131
 
1.7%
8.631
 
1.7%
8.131
 
1.7%
7.883
5.0%
7.751
 
1.7%
7.631
 
1.7%
7.381
 
1.7%
7.252
3.3%
7.134
6.7%

Speed
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.815
Minimum0.63
Maximum9.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:34.450990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.63
5-th percentile0.994
Q13
median5.13
Q36.41
95-th percentile7.7565
Maximum9.88
Range9.25
Interquartile range (IQR)3.41

Descriptive statistics

Standard deviation2.2664035
Coefficient of variation (CV)0.47069647
Kurtosis-0.67369252
Mean4.815
Median Absolute Deviation (MAD)1.62
Skewness-0.2453893
Sum288.9
Variance5.1365847
MonotonicityNot monotonic
2025-11-24T16:05:34.547230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5.137
 
11.7%
6.753
 
5.0%
33
 
5.0%
5.53
 
5.0%
7.752
 
3.3%
7.252
 
3.3%
6.382
 
3.3%
4.252
 
3.3%
52
 
3.3%
6.132
 
3.3%
Other values (29)32
53.3%
ValueCountFrequency (%)
0.631
1.7%
0.751
1.7%
0.881
1.7%
11
1.7%
1.131
1.7%
1.251
1.7%
1.381
1.7%
1.51
1.7%
1.632
3.3%
1.751
1.7%
ValueCountFrequency (%)
9.881
 
1.7%
8.881
 
1.7%
7.881
 
1.7%
7.752
3.3%
7.51
 
1.7%
7.381
 
1.7%
7.252
3.3%
7.131
 
1.7%
71
 
1.7%
6.753
5.0%

Agility
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.765
Minimum1
Maximum8.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:34.636003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6235
Q13.4375
median4.755
Q36.38
95-th percentile7.636
Maximum8.25
Range7.25
Interquartile range (IQR)2.9425

Descriptive statistics

Standard deviation1.927273
Coefficient of variation (CV)0.40446444
Kurtosis-1.0057379
Mean4.765
Median Absolute Deviation (MAD)1.565
Skewness-0.15171284
Sum285.9
Variance3.7143814
MonotonicityNot monotonic
2025-11-24T16:05:34.730878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
46
 
10.0%
6.384
 
6.7%
63
 
5.0%
3.633
 
5.0%
6.633
 
5.0%
6.133
 
5.0%
2.53
 
5.0%
2.752
 
3.3%
5.632
 
3.3%
4.132
 
3.3%
Other values (29)29
48.3%
ValueCountFrequency (%)
11
 
1.7%
1.131
 
1.7%
1.51
 
1.7%
1.631
 
1.7%
1.751
 
1.7%
1.881
 
1.7%
2.251
 
1.7%
2.53
5.0%
2.752
3.3%
2.881
 
1.7%
ValueCountFrequency (%)
8.251
 
1.7%
8.131
 
1.7%
7.751
 
1.7%
7.631
 
1.7%
7.381
 
1.7%
7.251
 
1.7%
71
 
1.7%
6.881
 
1.7%
6.751
 
1.7%
6.633
5.0%

Flexibility
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7231667
Minimum1.13
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:34.826385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.13
5-th percentile2.3485
Q13.8475
median4.75
Q35.5325
95-th percentile7.5375
Maximum10
Range8.87
Interquartile range (IQR)1.685

Descriptive statistics

Standard deviation1.7043688
Coefficient of variation (CV)0.36085298
Kurtosis0.89492905
Mean4.7231667
Median Absolute Deviation (MAD)0.875
Skewness0.55956217
Sum283.39
Variance2.9048729
MonotonicityNot monotonic
2025-11-24T16:05:34.925917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
5.634
 
6.7%
2.634
 
6.7%
4.754
 
6.7%
4.383
 
5.0%
53
 
5.0%
5.133
 
5.0%
5.53
 
5.0%
4.253
 
5.0%
43
 
5.0%
7.52
 
3.3%
Other values (23)28
46.7%
ValueCountFrequency (%)
1.131
 
1.7%
1.752
3.3%
2.381
 
1.7%
2.634
6.7%
2.882
3.3%
31
 
1.7%
3.251
 
1.7%
3.381
 
1.7%
3.51
 
1.7%
3.751
 
1.7%
ValueCountFrequency (%)
101
 
1.7%
8.51
 
1.7%
8.251
 
1.7%
7.52
3.3%
72
3.3%
6.881
 
1.7%
6.631
 
1.7%
5.881
 
1.7%
5.751
 
1.7%
5.634
6.7%

Nerve
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6148333
Minimum0.88
Maximum9.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:35.018458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.88
5-th percentile1.6175
Q12.5975
median4.19
Q36.5325
95-th percentile8.886
Maximum9.88
Range9
Interquartile range (IQR)3.935

Descriptive statistics

Standard deviation2.4426455
Coefficient of variation (CV)0.52930308
Kurtosis-0.921817
Mean4.6148333
Median Absolute Deviation (MAD)1.81
Skewness0.46577015
Sum276.89
Variance5.9665169
MonotonicityNot monotonic
2025-11-24T16:05:35.112846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
6.633
 
5.0%
23
 
5.0%
4.883
 
5.0%
3.633
 
5.0%
2.753
 
5.0%
62
 
3.3%
32
 
3.3%
5.882
 
3.3%
1.752
 
3.3%
2.632
 
3.3%
Other values (30)35
58.3%
ValueCountFrequency (%)
0.881
 
1.7%
1.251
 
1.7%
1.381
 
1.7%
1.632
3.3%
1.752
3.3%
23
5.0%
2.251
 
1.7%
2.382
3.3%
2.52
3.3%
2.632
3.3%
ValueCountFrequency (%)
9.881
1.7%
9.51
1.7%
91
1.7%
8.881
1.7%
8.382
3.3%
8.251
1.7%
81
1.7%
7.881
1.7%
7.751
1.7%
7.51
1.7%

Durability
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6128333
Minimum0.75
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:35.198261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile1.4875
Q13.38
median4.63
Q35.7825
95-th percentile7.8985
Maximum8.5
Range7.75
Interquartile range (IQR)2.4025

Descriptive statistics

Standard deviation1.8821022
Coefficient of variation (CV)0.40801436
Kurtosis-0.31225551
Mean4.6128333
Median Absolute Deviation (MAD)1.25
Skewness0.10186303
Sum276.77
Variance3.5423088
MonotonicityNot monotonic
2025-11-24T16:05:35.299393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4.635
 
8.3%
53
 
5.0%
6.883
 
5.0%
3.253
 
5.0%
8.52
 
3.3%
4.252
 
3.3%
5.52
 
3.3%
3.52
 
3.3%
4.52
 
3.3%
4.752
 
3.3%
Other values (31)34
56.7%
ValueCountFrequency (%)
0.751
1.7%
0.881
1.7%
1.251
1.7%
1.51
1.7%
1.882
3.3%
2.131
1.7%
2.381
1.7%
2.631
1.7%
2.751
1.7%
2.881
1.7%
ValueCountFrequency (%)
8.52
3.3%
8.251
 
1.7%
7.881
 
1.7%
7.751
 
1.7%
7.381
 
1.7%
6.883
5.0%
6.751
 
1.7%
6.381
 
1.7%
6.251
 
1.7%
6.131
 
1.7%

Hand-eye coordination
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9548333
Minimum1.88
Maximum9.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:35.570363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.88
5-th percentile2.3735
Q13.13
median4.44
Q36.63
95-th percentile8.38
Maximum9.25
Range7.37
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation1.966328
Coefficient of variation (CV)0.39685048
Kurtosis-0.94595037
Mean4.9548333
Median Absolute Deviation (MAD)1.56
Skewness0.40092155
Sum297.29
Variance3.8664457
MonotonicityNot monotonic
2025-11-24T16:05:35.672168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2.885
 
8.3%
4.383
 
5.0%
4.133
 
5.0%
3.633
 
5.0%
7.253
 
5.0%
7.52
 
3.3%
6.632
 
3.3%
3.132
 
3.3%
42
 
3.3%
62
 
3.3%
Other values (28)33
55.0%
ValueCountFrequency (%)
1.881
 
1.7%
2.131
 
1.7%
2.251
 
1.7%
2.381
 
1.7%
2.51
 
1.7%
2.631
 
1.7%
2.751
 
1.7%
2.885
8.3%
32
 
3.3%
3.132
 
3.3%
ValueCountFrequency (%)
9.251
 
1.7%
8.881
 
1.7%
8.382
3.3%
81
 
1.7%
7.881
 
1.7%
7.52
3.3%
7.253
5.0%
7.131
 
1.7%
71
 
1.7%
6.751
 
1.7%

Analytical Aptitude
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7673333
Minimum2.25
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:35.775674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.25
5-th percentile2.6175
Q13.4375
median4.25
Q36.16
95-th percentile7.386
Maximum7.5
Range5.25
Interquartile range (IQR)2.7225

Descriptive statistics

Standard deviation1.57683
Coefficient of variation (CV)0.33075723
Kurtosis-1.2879645
Mean4.7673333
Median Absolute Deviation (MAD)1.37
Skewness0.20088334
Sum286.04
Variance2.4863928
MonotonicityNot monotonic
2025-11-24T16:05:35.868104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5.635
 
8.3%
7.53
 
5.0%
6.883
 
5.0%
6.53
 
5.0%
4.253
 
5.0%
4.133
 
5.0%
3.753
 
5.0%
2.883
 
5.0%
33
 
5.0%
3.133
 
5.0%
Other values (20)28
46.7%
ValueCountFrequency (%)
2.251
 
1.7%
2.382
3.3%
2.631
 
1.7%
2.883
5.0%
33
5.0%
3.133
5.0%
3.252
3.3%
3.52
3.3%
3.631
 
1.7%
3.753
5.0%
ValueCountFrequency (%)
7.53
5.0%
7.381
 
1.7%
7.131
 
1.7%
6.883
5.0%
6.751
 
1.7%
6.53
5.0%
6.382
3.3%
6.251
 
1.7%
6.131
 
1.7%
61
 
1.7%

Total
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.983333
Minimum14.5
Maximum72.375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:35.961122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile25.35
Q144.09375
median48
Q356.03125
95-th percentile67.9
Maximum72.375
Range57.875
Interquartile range (IQR)11.9375

Descriptive statistics

Standard deviation12.170519
Coefficient of variation (CV)0.24846246
Kurtosis0.48303486
Mean48.983333
Median Absolute Deviation (MAD)6.375
Skewness-0.537667
Sum2939
Variance148.12154
MonotonicityDecreasing
2025-11-24T16:05:36.083452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.8752
 
3.3%
60.6252
 
3.3%
54.3752
 
3.3%
36.252
 
3.3%
462
 
3.3%
482
 
3.3%
63.3751
 
1.7%
63.51
 
1.7%
67.8751
 
1.7%
62.751
 
1.7%
Other values (44)44
73.3%
ValueCountFrequency (%)
14.51
1.7%
21.51
1.7%
24.8751
1.7%
25.3751
1.7%
27.51
1.7%
30.6251
1.7%
31.751
1.7%
36.252
3.3%
37.8751
1.7%
41.6251
1.7%
ValueCountFrequency (%)
72.3751
1.7%
71.751
1.7%
68.3751
1.7%
67.8751
1.7%
63.51
1.7%
63.3751
1.7%
62.751
1.7%
62.51
1.7%
62.251
1.7%
61.51
1.7%

Rank
Real number (ℝ)

High correlation  Uniform 

Distinct54
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.4
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size612.0 B
2025-11-24T16:05:36.193501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q115.75
median30
Q345.25
95-th percentile57.05
Maximum60
Range59
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.473661
Coefficient of variation (CV)0.57479147
Kurtosis-1.2055722
Mean30.4
Median Absolute Deviation (MAD)15
Skewness0.0057869445
Sum1824
Variance305.32881
MonotonicityIncreasing
2025-11-24T16:05:36.298574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172
 
3.3%
112
 
3.3%
202
 
3.3%
522
 
3.3%
402
 
3.3%
302
 
3.3%
61
 
1.7%
51
 
1.7%
41
 
1.7%
71
 
1.7%
Other values (44)44
73.3%
ValueCountFrequency (%)
11
1.7%
21
1.7%
31
1.7%
41
1.7%
51
1.7%
61
1.7%
71
1.7%
81
1.7%
91
1.7%
101
1.7%
ValueCountFrequency (%)
601
1.7%
591
1.7%
581
1.7%
571
1.7%
561
1.7%
551
1.7%
541
1.7%
522
3.3%
511
1.7%
501
1.7%

Interactions

2025-11-24T16:05:32.198588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:21.843661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:22.631722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:23.388978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:24.175417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:25.103892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:25.939520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:26.740495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:27.657237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:28.777613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:30.468775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:31.361300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:32.260722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:21.907590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:23.444843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:24.243458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:25.165911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:26.002244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:27.744850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:31.434004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:32.323659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:32.882034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:31.931382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:32.957760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:22.422838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:23.188825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:23.970923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:32.001803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:33.021564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:24.039731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-24T16:05:30.312558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:31.203387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:32.070817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:33.084626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:22.565768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:23.319974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:24.105210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:24.885985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:25.864952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:26.675877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:27.579380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:28.706434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:30.389160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:31.276069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-24T16:05:32.134582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-24T16:05:36.388798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgilityAnalytical AptitudeDurabilityEnduranceFlexibilityHand-eye coordinationNervePowerRankSpeedStrengthTotal
Agility1.0000.4780.5290.4730.6610.5120.2770.399-0.8080.6030.2440.808
Analytical Aptitude0.4781.0000.2850.3810.0220.7290.1720.067-0.5310.217-0.0640.531
Durability0.5290.2851.0000.6140.3350.1020.6190.466-0.7770.4430.6450.777
Endurance0.4730.3810.6141.0000.2250.0660.1030.324-0.6240.5320.4290.624
Flexibility0.6610.0220.3350.2251.0000.0730.2470.312-0.5310.4350.2170.531
Hand-eye coordination0.5120.7290.1020.0660.0731.0000.1080.056-0.4300.081-0.1620.430
Nerve0.2770.1720.6190.1030.2470.1081.0000.225-0.5120.0900.3650.512
Power0.3990.0670.4660.3240.3120.0560.2251.000-0.6440.5920.8340.644
Rank-0.808-0.531-0.777-0.624-0.531-0.430-0.512-0.6441.000-0.669-0.589-1.000
Speed0.6030.2170.4430.5320.4350.0810.0900.592-0.6691.0000.4670.669
Strength0.244-0.0640.6450.4290.217-0.1620.3650.834-0.5890.4671.0000.589
Total0.8080.5310.7770.6240.5310.4300.5120.644-1.0000.6690.5891.000

Missing values

2025-11-24T16:05:33.191324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-24T16:05:33.293088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SPORTEnduranceStrengthPowerSpeedAgilityFlexibilityNerveDurabilityHand-eye coordinationAnalytical AptitudeTotalRank
0Boxing8.638.138.636.386.254.388.888.507.005.6372.3751
1Ice Hockey7.257.137.887.757.634.886.008.257.507.5071.7502
2Football5.388.638.137.136.384.387.258.505.507.1368.3753
3Basketball7.386.256.507.258.135.634.137.757.507.3867.8754
4Wrestling6.638.387.135.136.387.505.006.754.256.3863.5005
5Martial Arts5.005.887.756.386.007.006.635.886.006.8863.3756
6Tennis7.255.137.136.757.755.633.005.008.386.7562.7507
7Gymnastics5.386.136.635.006.3810.007.506.884.504.1362.5008
8Baseball/Softball4.635.757.636.506.754.755.135.639.256.2562.2509
9Soccer7.754.505.137.258.254.753.636.256.507.5061.50010
SPORTEnduranceStrengthPowerSpeedAgilityFlexibilityNerveDurabilityHand-eye coordinationAnalytical AptitudeTotalRank
50Golf3.253.886.131.631.754.002.502.386.006.3837.87551
51Cheerleading3.633.633.382.254.137.503.633.382.502.2536.25052
52Roller Skating4.753.384.005.134.003.502.633.382.882.6336.25052
53Equestrian3.383.251.751.252.502.886.002.752.885.1331.75054
54Archery2.884.503.131.131.632.632.752.136.633.2530.62555
55Curling2.252.632.501.502.252.631.751.504.885.6327.50056
56Bowling2.252.753.381.001.882.381.631.254.754.1325.37557
57Shooting2.252.501.380.881.131.752.381.886.754.0024.87558
58Billiards1.001.001.750.751.002.631.630.755.255.7521.50059
59Fishing1.381.631.250.631.501.130.880.882.382.8814.50060